{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "import tensorflow as tf\n",
    "from collections import Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['negative', 'positive']\n",
      "10662\n",
      "10662\n"
     ]
    }
   ],
   "source": [
    "trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')\n",
    "trainset.data, trainset.target = separate_dataset(trainset,1.0)\n",
    "print(trainset.target_names)\n",
    "print(len(trainset.data))\n",
    "print(len(trainset.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 2\n",
    "n_topics = 10\n",
    "embedding_size = 128\n",
    "epoch = 5\n",
    "switch_loss = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LDA2VEC:\n",
    "    def __init__(\n",
    "        self,\n",
    "        num_unique_documents,\n",
    "        vocab_size,\n",
    "        num_topics,\n",
    "        freqs,\n",
    "        embedding_size = 128,\n",
    "        num_sampled = 40,\n",
    "        learning_rate = 1e-3,\n",
    "        lmbda = 150.0,\n",
    "        alpha = None,\n",
    "        power = 0.75,\n",
    "        batch_size = 32,\n",
    "        clip_gradients = 5.0,\n",
    "        **kwargs\n",
    "    ):\n",
    "        moving_avgs = tf.train.ExponentialMovingAverage(0.9)\n",
    "        self.batch_size = batch_size\n",
    "        self.freqs = freqs\n",
    "        self.sess = tf.InteractiveSession()\n",
    "\n",
    "        self.X = tf.placeholder(tf.int32, shape = [None])\n",
    "        self.Y = tf.placeholder(tf.int64, shape = [None])\n",
    "        self.DOC = tf.placeholder(tf.int32, shape = [None])\n",
    "        step = tf.Variable(0, trainable = False, name = 'global_step')\n",
    "        self.switch_loss = tf.Variable(0, trainable = False)\n",
    "        train_labels = tf.reshape(self.Y, [-1, 1])\n",
    "        sampler = tf.nn.fixed_unigram_candidate_sampler(\n",
    "            train_labels,\n",
    "            num_true = 1,\n",
    "            num_sampled = num_sampled,\n",
    "            unique = True,\n",
    "            range_max = vocab_size,\n",
    "            distortion = power,\n",
    "            unigrams = self.freqs,\n",
    "        )\n",
    "\n",
    "        self.word_embedding = tf.Variable(\n",
    "            tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0)\n",
    "        )\n",
    "        self.nce_weights = tf.Variable(\n",
    "            tf.truncated_normal(\n",
    "                [vocab_size, embedding_size],\n",
    "                stddev = tf.sqrt(1 / embedding_size),\n",
    "            )\n",
    "        )\n",
    "        self.nce_biases = tf.Variable(tf.zeros([vocab_size]))\n",
    "        scalar = 1 / np.sqrt(num_unique_documents + num_topics)\n",
    "        self.doc_embedding = tf.Variable(\n",
    "            tf.random_normal(\n",
    "                [num_unique_documents, num_topics],\n",
    "                mean = 0,\n",
    "                stddev = 50 * scalar,\n",
    "            )\n",
    "        )\n",
    "        self.topic_embedding = tf.get_variable(\n",
    "            'topic_embedding',\n",
    "            shape = [num_topics, embedding_size],\n",
    "            dtype = tf.float32,\n",
    "            initializer = tf.orthogonal_initializer(gain = scalar),\n",
    "        )\n",
    "        pivot = tf.nn.embedding_lookup(self.word_embedding, self.X)\n",
    "        proportions = tf.nn.embedding_lookup(self.doc_embedding, self.DOC)\n",
    "        doc = tf.matmul(proportions, self.topic_embedding)\n",
    "        doc_context = doc\n",
    "        word_context = pivot\n",
    "        context = tf.add(word_context, doc_context)\n",
    "        loss_word2vec = tf.reduce_mean(\n",
    "            tf.nn.nce_loss(\n",
    "                weights = self.nce_weights,\n",
    "                biases = self.nce_biases,\n",
    "                labels = self.Y,\n",
    "                inputs = context,\n",
    "                num_sampled = num_sampled,\n",
    "                num_classes = vocab_size,\n",
    "                num_true = 1,\n",
    "                sampled_values = sampler,\n",
    "            )\n",
    "        )\n",
    "        self.fraction = tf.Variable(1, trainable = False, dtype = tf.float32)\n",
    "\n",
    "        n_topics = self.doc_embedding.get_shape()[1].value\n",
    "        log_proportions = tf.nn.log_softmax(self.doc_embedding)\n",
    "        if alpha is None:\n",
    "            alpha = 1.0 / n_topics\n",
    "        loss = -(alpha - 1) * log_proportions\n",
    "        prior = tf.reduce_sum(loss)\n",
    "\n",
    "        loss_lda = lmbda * self.fraction * prior\n",
    "        self.cost = tf.cond(\n",
    "            step < self.switch_loss,\n",
    "            lambda: loss_word2vec,\n",
    "            lambda: loss_word2vec + loss_lda,\n",
    "        )\n",
    "        loss_avgs_op = moving_avgs.apply([loss_lda, loss_word2vec, self.cost])\n",
    "        with tf.control_dependencies([loss_avgs_op]):\n",
    "            self.optimizer = tf.contrib.layers.optimize_loss(\n",
    "                self.cost,\n",
    "                tf.train.get_global_step(),\n",
    "                learning_rate,\n",
    "                'Adam',\n",
    "                clip_gradients = clip_gradients,\n",
    "            )\n",
    "        self.sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    def train(\n",
    "        self, pivot_words, target_words, doc_ids, num_epochs, switch_loss = 3\n",
    "    ):\n",
    "        from tqdm import tqdm\n",
    "\n",
    "        temp_fraction = self.batch_size / len(pivot_words)\n",
    "        self.sess.run(tf.assign(self.fraction, temp_fraction))\n",
    "        self.sess.run(tf.assign(self.switch_loss, switch_loss))\n",
    "        for e in range(num_epochs):\n",
    "            pbar = tqdm(\n",
    "                range(0, len(pivot_words), self.batch_size),\n",
    "                desc = 'minibatch loop',\n",
    "            )\n",
    "            for i in pbar:\n",
    "                batch_x = pivot_words[\n",
    "                    i : min(i + self.batch_size, len(pivot_words))\n",
    "                ]\n",
    "                batch_y = target_words[\n",
    "                    i : min(i + self.batch_size, len(pivot_words))\n",
    "                ]\n",
    "                batch_doc = doc_ids[\n",
    "                    i : min(i + self.batch_size, len(pivot_words))\n",
    "                ]\n",
    "                _, cost = self.sess.run(\n",
    "                    [self.optimizer, self.cost],\n",
    "                    feed_dict = {\n",
    "                        self.X: batch_x,\n",
    "                        self.Y: batch_y,\n",
    "                        self.DOC: batch_doc,\n",
    "                    },\n",
    "                )\n",
    "                pbar.set_postfix(cost = cost, epoch = e + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "def skipgrams(\n",
    "    sequence,\n",
    "    vocabulary_size,\n",
    "    window_size = 4,\n",
    "    negative_samples = 1.0,\n",
    "    shuffle = True,\n",
    "    categorical = False,\n",
    "    sampling_table = None,\n",
    "    seed = None,\n",
    "):\n",
    "    couples = []\n",
    "    labels = []\n",
    "    for i, wi in enumerate(sequence):\n",
    "        if not wi:\n",
    "            continue\n",
    "        if sampling_table is not None:\n",
    "            if sampling_table[wi] < random.random():\n",
    "                continue\n",
    "\n",
    "        window_start = max(0, i - window_size)\n",
    "        window_end = min(len(sequence), i + window_size + 1)\n",
    "        for j in range(window_start, window_end):\n",
    "            if j != i:\n",
    "                wj = sequence[j]\n",
    "                if not wj:\n",
    "                    continue\n",
    "                couples.append([wi, wj])\n",
    "                if categorical:\n",
    "                    labels.append([0, 1])\n",
    "                else:\n",
    "                    labels.append(1)\n",
    "\n",
    "    if negative_samples > 0:\n",
    "        num_negative_samples = int(len(labels) * negative_samples)\n",
    "        words = [c[0] for c in couples]\n",
    "        random.shuffle(words)\n",
    "\n",
    "        couples += [\n",
    "            [words[i % len(words)], random.randint(1, vocabulary_size - 1)]\n",
    "            for i in range(num_negative_samples)\n",
    "        ]\n",
    "        if categorical:\n",
    "            labels += [[1, 0]] * num_negative_samples\n",
    "        else:\n",
    "            labels += [0] * num_negative_samples\n",
    "\n",
    "    if shuffle:\n",
    "        if seed is None:\n",
    "            seed = random.randint(0, 10e6)\n",
    "        random.seed(seed)\n",
    "        random.shuffle(couples)\n",
    "        random.seed(seed)\n",
    "        random.shuffle(labels)\n",
    "\n",
    "    return couples, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bow = CountVectorizer().fit(trainset.data)\n",
    "transformed = bow.transform(trainset.data)\n",
    "idx_text_clean, len_idx_text_clean = [], []\n",
    "for text in transformed:\n",
    "    splitted = text.nonzero()[1]\n",
    "    idx_text_clean.append(splitted)\n",
    "    \n",
    "dictionary = {\n",
    "        i: no for no, i in enumerate(bow.get_feature_names())\n",
    "    }\n",
    "reversed_dictionary = {\n",
    "        no: i for no, i in enumerate(bow.get_feature_names())\n",
    "    }\n",
    "freqs = transformed.toarray().sum(axis = 0).tolist()\n",
    "doc_ids = np.arange(len(idx_text_clean))\n",
    "num_unique_documents = doc_ids.max()\n",
    "pivot_words, target_words, doc_ids = [], [], []\n",
    "for i, t in enumerate(idx_text_clean):\n",
    "    pairs, _ = skipgrams(\n",
    "            t,\n",
    "            vocabulary_size = len(dictionary),\n",
    "            window_size = window_size,\n",
    "            shuffle = True,\n",
    "            negative_samples = 0,\n",
    "        )\n",
    "    for pair in pairs:\n",
    "        temp_data = pair\n",
    "        pivot_words.append(temp_data[0])\n",
    "        target_words.append(temp_data[1])\n",
    "        doc_ids.append(i)\n",
    "pivot_words, target_words, doc_ids = shuffle(\n",
    "        pivot_words, target_words, doc_ids, random_state = 10\n",
    ")\n",
    "num_unique_documents = len(idx_text_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LDA2VEC(\n",
    "        num_unique_documents,\n",
    "        len(dictionary),\n",
    "        n_topics,\n",
    "        freqs,\n",
    "        embedding_size = embedding_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "minibatch loop: 100%|██████████| 11987/11987 [01:40<00:00, 118.55it/s, cost=-1.85e+4, epoch=1]\n",
      "minibatch loop: 100%|██████████| 11987/11987 [01:40<00:00, 119.28it/s, cost=-4.34e+4, epoch=2]\n",
      "minibatch loop: 100%|██████████| 11987/11987 [01:40<00:00, 119.51it/s, cost=-6.76e+4, epoch=3]\n",
      "minibatch loop: 100%|██████████| 11987/11987 [01:40<00:00, 119.69it/s, cost=-9.17e+4, epoch=4]\n",
      "minibatch loop: 100%|██████████| 11987/11987 [01:40<00:00, 119.71it/s, cost=-1.16e+5, epoch=5]\n"
     ]
    }
   ],
   "source": [
    "model.train(\n",
    "    pivot_words, target_words, doc_ids, epoch, switch_loss = switch_loss\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_embed = model.sess.run(model.doc_embedding)\n",
    "topic_embed = model.sess.run(model.topic_embedding)\n",
    "word_embed = model.sess.run(model.word_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10662, 10), (10, 128), (20306, 128))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_embed.shape, topic_embed.shape, word_embed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['friend', 0.44515836238861084],\n",
       " ['capture', 0.4315608739852905],\n",
       " ['american', 0.41776609420776367],\n",
       " ['art', 0.41060054302215576],\n",
       " ['cashin', 0.4037929177284241],\n",
       " ['awkwardly', 0.40309447050094604],\n",
       " ['gifted', 0.4017599821090698],\n",
       " ['brisk', 0.397861123085022],\n",
       " ['come', 0.38802099227905273]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word = 'beautiful'\n",
    "nn = NearestNeighbors(10, metric = 'cosine').fit(word_embed)\n",
    "distances, idx = nn.kneighbors(word_embed[dictionary[word]].reshape((1, -1)))\n",
    "word_list = []\n",
    "for i in range(1, idx.shape[1]):\n",
    "    word_list.append([reversed_dictionary[idx[0, i]], 1 - distances[0, i]])\n",
    "word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic 1 : perch imaxy regions addessi astronauts seacoast fisk divining wellnigh\n",
      "topic 2 : bitchy confirming worldview terrorists clich diatribe filter exquisitely billy\n",
      "topic 3 : livingroom marivauxs privates plunges andespeciallyto establishing association emi auteils\n",
      "topic 4 : unfathomable utilizing twinkling tuna unbroken bigwave capitalizes awards leash\n",
      "topic 5 : senior nicholson massoud molto widen disgracefully racked bearing organize\n",
      "topic 6 : brats clutchy utilizing versace upfront andie sterotypes triplecrosses shecute\n",
      "topic 7 : auteils buoy bastards gobble upfront transforma victimized pre911 sidewalks\n",
      "topic 8 : smokey barrie venomous trudge arguing lux bludgeon predecesora weasels\n",
      "topic 9 : skeleton cradles unholy ryder indieflick predawn combo realitysnubbing augmented\n",
      "topic 10 : bottomfeeder disorienting bedroom topkapi sandal convict shield becks mixer\n"
     ]
    }
   ],
   "source": [
    "components = topic_embed.dot(word_embed.T)\n",
    "for no, topic in enumerate(components):\n",
    "    topic_string = ' '.join([reversed_dictionary[i]\n",
    "              for i in topic.argsort()[: -10 : -1]])\n",
    "    print('topic %d : %s'%(no + 1, topic_string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from MulticoreTSNE import MulticoreTSNE as TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_jobs=4)\n",
    "X = tsne.fit_transform(doc_embed.astype('float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "unique_label = np.unique(trainset.target)\n",
    "encoded = LabelEncoder().fit_transform(trainset.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "colors = ['red','blue']\n",
    "for no, i in enumerate(unique_label):\n",
    "    plt.scatter(X[encoded==i,0],X[encoded==i,1],label=unique_label[no],color=colors[no])\n",
    "plt.legend(['negative','positive'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
